UKERC Technology and Policy Assessment Cost Methodologies

i

UKERC Technology and Policy

Assessment

Cost Methodologies Project:

CCS Case Study

Working Paper

February 2012: REF UKERC/WP/TPA/2013/004

Felicity Jones, Imperial College Centre for Energy Policy and Technology

This document has been prepared to enable results of on-going work to be made available

rapidly. It has not been subject to review and approval, and does not have the authority of a full

Research Report.

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UK Energy Research Centre UKERC/WP/TPA/2013/004

T H E U K E N E R G Y R E S E A R C H C E N T R E

The UK Energy Research Centre carries out world-class research into sustainable future

energy systems.

It is the hub of UK energy research and the gateway between the UK and the

international energy research communities. Our interdisciplinary, whole systems

research informs UK policy development and research strategy.

www.ukerc.ac.uk

The Technology and Policy Assessment (TPA) Theme of UKERC

The TPA was set up to inform decision-making processes and address key controversies

in the energy field. It aims to provide authoritative and accessible reports that set very

high standards for rigour and transparency. Subjects are chosen after extensive

consultation with energy sector stakeholders and upon the recommendation of the TPA

Advisory Group, which is comprised of independent experts from government, academia

and the private sector.

The primary objective of the TPA is to provide a thorough review of the current state of

knowledge. New research, such as modelling or primary data gathering may be carried

out when essential. It also aims to explain its findings in a way that is accessible to non-

technical readers and is useful to policymakers.

This working paper was produced as part of the TPA Cost Methodologies project.

http://www.ukerc.ac.uk/

Contents

1. INTRODUCTION .......................................................................................................1

2. CURRENT COST ESTIMATION ....................................................................................2

TRENDS .......................................................................................................................... 2

DISCUSSION – COST ESCALATION .......................................................................................... 4

DISCUSSION – VARIATION ................................................................................................... 7

3. FORECAST COST TRAJECTORIES ................................................................................9

TRENDS .......................................................................................................................... 9

DISCUSSION .................................................................................................................. 10

4. CONCLUSIONS ....................................................................................................... 11

REFERENCES .............................................................................................................. 13

APPENDIX .................................................................................................................. 17

SYSTEMATIC SEARCH ....................................................................................................... 17

DATA ANALYSIS .............................................................................................................. 17

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1. Introduction

Global aspirations for carbon capture and storage (CCS) technologies are high.

According to the International Energy Agency’s BLUE map scenario, achieving a 50%

global greenhouse gas reduction by 2050 requires CCS-fitted plant to account for 17%

of total electricity generation (IEA, 2009)1. Yet, despite its central role in future energy

scenarios, CCS is still yet to be demonstrated at utility scale. This means that CCS cost

estimates are not informed by practical experience of building commercial-scale plant.

With high aspirations present and utility-scale empirical data absent, CCS technologies

provide an interesting case study for analysing cost estimation methodologies. As such,

this Working Paper examines global trends in current and future projections of CCS

costs in the power sector, aiming to:

Examine key trends in contemporary and forecast CCS cost estimates;

Understand the drivers underlying these key trends; and

Identify implications for CCS cost estimation methodologies.

A systematic literature review was conducted as a basis for analysing CCS cost

estimates, with approximately fifty relevant academic articles and grey literature reports

being identified (as detailed in the Appendix). The focus for analysis was estimates of

levelised and capex costs for CCS. It is recognised that the decision to analyse these

cost metrics – instead of CO2 avoidance costs – has implications for the relative

attractiveness of coal CCS and gas CCS technologies. However, these metrics bring the

benefit of enabling the comparison of CCS with other power sector technologies

analysed in this Working Paper series (UKERC, 2011).

The paper begins by considering trends in current cost estimates for CCS (Section 2),

and then progresses to examining future projections (Section 3). Following this,

implications for CCS cost estimation methodologies are identified (Section 4).

1 Scenario assumes that greenhouse gas reduction is achieved by 2050 at least cost,

relative to 2005.

2


2. Current cost estimation

The literature adopted a range of approaches to estimating CCS costs – spanning

engineering assessments, Front-End Engineering and Design (FEED) studies, the

application of experience curves, expert elicitation, and derivation from secondary

sources. This Section presents contemporary CCS cost estimates from the literature,

identifying trends and then considering the potential drivers of such trends.

Trends

Figure 1 below plots contemporary levelised cost estimates for CCS technologies from

36 different sources. The estimates reflect the cost of both CCS and the generating

power plant (rather than just the additional cost of CO2 pollution abatement).

Figure 1: Current levelised cost estimates of CCS technologies since 2000.2

2 The graph illustrates cost estimates for five CCS technologies (and associated

generating plant), distinguishing between CO2 capture process and fuel type. It is

important to distinguish between differing CCS technologies since they have differing

cost structures; for instance, gas CCS tends to be less capital intensive than coal CCS.

Figures are presented in 2011 GBP – see Appendix for details of data selection and

treatment.

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The graph indicates that there has been a substantial increase in levelised cost

estimates for all CCS technologies since 2005. In addition, the graph displays

widespread variation in cost estimates for CCS technologies, even within the same year.

Figures 2 and 3 below demonstrate the extent to which CCS cost escalation is associated

with the cost escalation of the underlying generating power plants, such as combined

cycle gas turbines (CCGT) and advanced supercritical (ASC) coal-fired plant. The graphs

indicate that the capex costs of CCS-fitted generating plants roughly move in parallel

with the capex costs of unabated power plants.

Figure 2: Estimated capital costs for CCGT plant, both unabated and abated.

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Figure 3: Estimated capital costs for coal plant, both unabated and abated (excluding

integrated gasification combined cycle).

Discussion – cost escalation

This Section analyses why CCS levelised cost estimates have increased over time.

Adopting the terminology of this Working Paper series, it distinguishes between

methodological, exogenous and endogenous drivers.

Exogenous drivers: broad, macroeconomic drivers that are external to the power

sector – which policymakers and industry have limited scope to mitigate.

Endogenous drivers: drivers emerging from within the power sector – which

policymakers and industry can potentially mitigate.

Methodological drivers: drivers arising from the way in which costs are estimated

– rather than reflecting ‘real-life’ phenomena.

Exogenous drivers

From the early 2000s until 2008, high global demand dramatically pushed up the cost

of raw materials, such as steel, cement and copper (Davison and Thambimuthu, 2009).

This in turn led to increased estimates of construction costs, and in particular the costs

of constructing coal- and gas-fired power plants (rather than specifically CCS

technology). Since the economic downturn, commodity prices have reduced (DoE/NETL,

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2010). However, this has been counteracted by ongoing increases in operating costs,

through rising fuel prices (IEA, 2010).

Endogenous drivers

Although not strictly a reflection of bottom-up costs, supply chain bottlenecks have

significantly increased engineering, procurement and construction (EPC) prices for coal

and gas power plants. Full order books for vendors and manufacturing capacity

constraints have increased prices for plant components (Mott MacDonald, 2010), and

caused delivery delays that have increased project financing costs (Chupka and Basheda,

2007).

Such supply chain bottlenecks have affected the price of generating plant to which CCS

is fitted, rather than that of CCS technology itself. Nonetheless, by increasing the prices

of the generating plant, market congestion has led to an increase in overall price

estimates for CCS-fitted power plants. Since advanced supercritical coal plant have been

particularly vulnerable to supply chain bottlenecks, the effect on post-combustion coal

CCS is particularly pronounced, increasing estimates by almost 17%. This is illustrated in

Figure 4 below.

Figure 4: The estimated ‘congestion premium’ for CCS cost estimates arising from

supply chain bottlenecks. Based on estimates by Mott MacDonald (2010).

Methodological drivers

In addition to exogenous and endogenous cost drivers, a further cause of cost

escalation is the apparent tendency of early-stage engineering assessments to

demonstrate appraisal optimism, which then appears to be ‘corrected’ in later cost

6


estimates. The term ‘appraisal optimism’ refers to the propensity of prospective

developers to underestimate costs – be it due to natural enthusiasm or due to the

incentive of securing public funding (Scrase and Watson, 2009). In particular, it appears

that early cost estimates of projects tend to be more optimistic – i.e. lower – than later

estimates, due to over-simplified system designs and underestimating risks. Later, once

projects are defined in greater detail and costs are more rigorously calculated, estimates

tend to be revised upwards.

The case study of retrofitting CCS to one of the four units at Longannet coal-fired power

station helps to illustrate this point (ScottishPower CCS Consortium, 2011). Figure 5

below compares the estimates of this CCS retrofit before detailed Front-End Engineering

Design work (‘Outline Solution’) and after more detailed engineering assessments

(‘post-FEED’).

Figure 5: Estimated project capital costs prior to, and following, Front-End Engineering

Design (FEED) study for CCS retrofit at Longannet coal-fired power station

(ScottishPower CCS Consortium, 2011).

The figure illustrates that overall estimated project capex increased by 13.6% from the

initial Outline Solution. The Consortium explained that the FEED study revealed a need

for a more sophisticated capture and transportation system, such as the requirement for

tunnelling under the Firth of Forth river instead of the horizontal directional drilling

originally proposed. Another notable driver of the increased post-FEED cost estimates

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was the increase in allowance for risk and contingency costs by 89.5%, ‘reflecting the

better identification and quantification of risks’ (ScottishPower CCS Consortium, 2011).

Overall, the Outline Solution could be interpreted as displaying initial appraisal

optimism, which is then revised and made more ‘realistic’ post-FEED.

The increased magnitude of risk premiums in CCS cost estimations over time is also

noted in other reports as a driver of the upward revision of cost estimates. Indeed, there

has been a move to factoring in significantly higher contingency figures than was the

case in the early 2000s, to reflect first-of-a-kind costs (EPRI, 2007). For instance, a

revised cost estimate of CCS retrofit of Kårstø gas-fired power station in Norway factors

in substantially higher contingency reserves than the initial cost estimate; this is in

recognition that CCS investments are ‘mega-projects’ with substantial risks of cost

overruns (Osmundsen and Emhjellen, 2010).

Discussion – variation

In addition to cost escalation, Figure 1 indicates substantial variation in CCS current cost

estimates. It would appear that part of this variation is reflective of inherent variation,

since the choice of CCS technology and location brings distinct costs. However, the

remaining variation in cost estimates appears to reflect not real-life differences, but

rather imperfect knowledge and unstandardized methodologies. These are discussed

below.

Inherent variation

There are two drivers of inherent cost variation:

The specific design of a project; and

The location of the project.

In terms of project design, the choice of capture technology – post-combustion, pre-

combustion or oxyfuel – significantly affects the cost profile of projects, as does the

capture efficiency and project size (Chen and Rubin, 2009). More broadly, the specific

financing arrangements associated with the project are crucial. Factors such as the cost

of capital and the ability of the project developer to manage outgoings are significant

(Mott MacDonald, 2010, Simbeck and Beecy, 2011).

Meanwhile, locational differences can also significantly affect CCS costs. In particular,

geographical location substantially affects the transportation and storage options

available – for instance, whether there is potential to reduce transport network costs

through clustering with other CCS installations. Moreover, the cost and type of fuel

varies significantly depending on location; for instance, levelised cost estimates for gas

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CCS in Saudi Arabia are relatively low due to cheap local natural gas supplies

(WorleyParsons, 2009).

Other locationally-differentiated drivers of costs are labour rates, which are particularly

low in China and India (WorleyParsons, 2009); legal costs such as acquiring permits and

licences; and national policies such as carbon taxes. The local characteristics of each

particular market can also affect the cost of financing, which is especially significant for

coal CCS given its capital intensity (IEA, 2010).

Imperfect knowledge

In addition to inherent variation, there is a significant degree of uncertainty surrounding

cost estimates for CCS, since it is not possible to verify estimates with empirical

commercial-scale cost data (Shackley et al. 2009). Although many of the technology

components are mature, CCS as an integrated technology is itself immature, leading to

high levels of uncertainty about performance (Giovanni and Richards, 2010). Other key

uncertainties include the expected economic life of the plant and how it will be

operated, which leads to differing assumptions about the levelisation period and load

factors (Global CCS Institute, 2011b, Chen and Rubin, 2009). Future fuel prices are also

highly uncertain (Davison and Thambimuthu, 2009). This uncertainty about the

appropriate values of key input variables can lead to widely varying cost estimates,

especially since CCS costs tend to demonstrate a high sensitivity to fuel prices and load

factors (Mott MacDonald, 2010).

Methodological

A further reason for the range in current cost estimates is methodological. The Global

CCS Institute (2011a) suggests that the differing methodologies used for calculating CCS

costs limits the comparability of different studies. This point was also a key theme of a

CCS Cost Workshop organised by the IEA (2011), with the conference proceedings

indicating that the diverse group of actors estimating CCS costs adopt differing

methodologies and use differing assumptions for underlying economic parameters.

For instance, it is striking that many of the papers reviewed did not factor in costs for

CO2 transportation, storage and monitoring, instead focusing on CO2 capture only. As

Rubin et al (2007a) highlight, this omission can lead to differing conclusions about the

relative total cost of different CCS technologies. Although the capture stage dominates

the total cost of the CCS process (Kheshgi et al., 2010), nonetheless the additional £5-

10/kWh cost associated with CO2 transportation and storage is significant. Another

example of inconsistency between cost estimates was the presentation of the year to

which the cost estimate applies – with some reports focusing on the commissioning

date, others the date of first capital investment, and others not clearly defining the year

to which the cost estimate applied.

9


3. Forecast cost trajectories

This Section examines forecast CCS cost trajectories until 2050, identifying trends and

then considering potential explanations for such trends.

Trends

Figure 6 below displays estimates of forecast cost trajectories for post-combustion gas

CCS. These future projections are mostly based on experience curve analysis, such as

the application of historical experience curves for flue gas desulphurisation to carbon

capture technology. The graph indicates consensus across the literature that gas CCS

costs are projected to steadily decrease over time. Although different sources project

differing rates of cost reduction, the literature mostly suggests relatively steady rates of

learning. Projections for other key CCS technologies (such as post-combustion and pre-

combustion coal CCS) demonstrate similar patterns.

Figure 6: Estimates of future capital costs of post-combustion gas CCS.3

3 The first cost estimate of each series (i.e. line) is typically a ‘current’ cost estimate; the

remaining estimates of each series are forecast projections of CCS costs.

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Discussion

The key drivers of cost reduction in future CCS cost projections are increased project

size, process integration and technological innovation (Al-Juaied and Whitmore, 2009),

reduced construction lead times (IEA, 2010), and the development of an efficient carbon

transport and storage network (PB Power, 2011). Yet the learning rate is expected to be

relatively steady, compared with technologies such as solar PV, partly due to the

relatively long lead times involved in designing and building CCS-fitted power plants

(Al-Juaied & Whitmore, 2009). For instance, Rubin et al (2004) suggest a learning rate of

12% on the capital cost for CCS systems, which excludes generating plant.

The overall potential for learning is limited by two key factors. Firstly, the generating

plants to which CCS will be fitted (with the exception of IGCC) are already technically

mature, and the various components of CCS have already been demonstrated separately,

which limits the scope for further improvement (Al-Juaied and Whitmore, 2009, Viebahn

et al., 2007). Secondly, lessons from nuclear power learning rates highlight the potential

for costs to continue to go up rather than down due to increased regulatory and safety

demands, particularly in regard to radioactive waste (Rai et al., 2010). Like nuclear

power, CCS also has ‘waste’ to dispose of (CO2), and it is possible that the costs

associated with safely storing CO2 might rise due to tighter compliance requirements in

the future (Mott MacDonald, 2011).

Experience curve methodology

It is important to acknowledge the limitations of the application of experience curves to

CCS. The approach of applying historical learning rates of analogous technologies such

as flue gas desulphurisation and selective catalytic reduction of NOx to CCS might

initially be considered apt, since they are all pollution abatement technologies operating

in the power sector. However, Rai et al (2010) highlight the contingent nature of

learning rates. Their work indicates that CCS learning depends not just on technological

potential, but also on the pace of roll-out, the regulatory regime and market structure.

The literature also indicates that there could be delays before learning begins. As Rubin

et al (2007) illustrate, there is historical precedent for technologies deployed in the

power sector to demonstrate cost increases during early commercialisation, suggesting

that CCS costs could rise before they fall.

In addition, the application of experience curves depends on assumptions about future

CCS deployment levels, since learning rates describe a relationship between cost

reduction and installed capacity. However, future CCS installed capacity is itself highly

uncertain. Rubin et al (2007b) make projections for future costs after 100GW of installed

CCS capacity, yet it is not known when CCS installed capacity will reach this level.

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4. Conclusions

This paper illustrates that there has been both significant escalation and substantial

variation in CCS cost estimates. The literature also displays a consensus that, with

deployment, CCS costs are expected to steadily decline. Some key drivers of cost

estimates are exogenous (e.g. commodity prices), whilst others are endogenous (e.g.

supply chain bottlenecks). Yet many are also methodological in nature, resulting from

the way in which estimates are calculated, rather than necessarily reflecting ‘real-life’

phenomena.

The paper’s findings have four key implications for the formulation and interpretation of

CCS cost estimation methodologies.

1. Standardisation of calculation and presentation

The data analysis of this paper was complicated by the lack of standardisation in the

way in which cost estimations are calculated and presented. As such, the author

reiterates the overarching call of the IEA’s CCS costs workshop (2011) of the need to

establish a common framework for cost estimation methodology and terminology. This

would help to facilitate comparison between differing cost estimates, and such

transparency would also enable easier identification of which assumptions/parameters

are driving the variation in cost estimates.

Standardisation would involve defining a list of items to be factored into the cost

calculation, and explicitly stating assumptions and key parameters. For instance, it

should be clear whether CO2 transportation costs assume point-to-point transportation,

or alternatively assume the economies of scale of a clustered transportation network. It

is also particularly important that the location of the project is clearly defined, due to

the inherent cost variation that arises from location – for instance, differing labour

productivity, carbon policies and CO2 storage sites.

2. Cautious application of experience curves

A further finding is the need to treat CCS experience curves with caution. CCS cost

estimates initially appear to have reflected appraisal optimism, with costs escalating as

the full complexities of system design become apparent. Even once CCS has been

demonstrated at scale, future cost projections should be treated with caution until the

likely deployment rates of CCS are better understood. Ultimately, cost reduction is

driven by increases in installed capacity rather than the mere progression of time, so

until the likely pattern of CCS deployment can be predicted with greater confidence,

future cost projections should be recognised as highly uncertain.

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3. Greater consideration of risk and congestion premiums

Often, bottom-up engineering assessments focus on CCS costs, rather than additionally

giving sufficient weight to risks and prices. The ScottishPower Consortium FEED Study

illustrates the highly important – though initially underestimated – allowance for risk

and contingency. Following a detailed FEED Study, the risk allowance was almost

doubled, thus accounting for 14.5% of total estimated capex costs. In addition, market

congestion and supply chain bottlenecks are often overlooked – particularly in academic

papers – yet could add up to 17% to the prices of CCS-fitted plant (Mott MacDonald,

2010). Risk premiums and congestion premiums are significant and thus deserve

greater attention in future CCS cost estimates.

4. Distinction between generating plant and CCS technologies

Many of the exogenous and endogenous factors discussed in this paper apply primarily

to generating plant, whereas the methodological factors tend to apply to CCS

technologies. It is important to make explicit this distinction between cost drivers

affecting generating plant (which can be quantified with greater certainty) and cost

drivers affecting CCS technology itself (which are less certain). The literature does not

always make this distinction clear.

Overall

Fundamentally, it is important to remember that CCS cost estimates are just that –

estimates, rather than actual cost data. As estimates, they are subject to significant

uncertainties. To a certain extent these uncertainties can be reduced. As argued in this

conclusion, calculations can be standardised; deployment rates can be better projected;

risk and congestion premiums can be more closely analysed; cost drivers of generating

plant and pollution abatement can be explicitly distinguished.

Yet ultimately, refined methodologies achieve only so much; they are a poor substitute

for empirical utility-scale experience. The best way to discover CCS costs, it would

seem, is to get building.

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Appendix

Systematic search

The findings of this Working Paper were informed by a systematic search using defined

search strings and Boolean terminology. Four academic databases were searched, as

detailed in Table A below.

Database Search string

Science Direct TITLE-ABSTR-KEY(“carbon capture and storage” or “CCS”) AND TITLE-

ABSTR-KEY(“cost”)

ISI Web of Science TS=(“carbon capture and storage” OR “CCS”) AND TS=(“cost”)

CSA Illumina KW=(“carbon capture and storage” OR “CCS”) AND KW=(“cost”)

Compendex (“carbon capture and storage” OR “CCS”) wn KY AND (“cost”) wn KY

Table A: Systematic review search strings.

Following this, the search engine Google was used to locate relevant documents in the

grey literature. Duplicates were removed, and documents of little or no relevance (based

on their title or abstract) were discarded. A small number of other relevant documents

were then additionally revealed via citation trails.

The documents were assigned a ‘relevance rating’ between 1 and 4 (with ‘1’ indicating a

very relevant document), based on their title or abstract. Articles with a relevance rating

of 1 (36 sources) were used as numerical data sources. Articles with a relevance rating

of 1 or 2 (over 50 sources) were used to understand the underlying drivers affecting

cost estimates.

Data analysis

Data characterisation

Geographical: Data was not limited geographically, though the literature search was

dominated by OECD – and particularly US – data sources, where most CCS cost

estimations appear to have been conducted.

Technology: Only numerical data applying to newly built CCS-fitted power stations was

analysed, rather than additionally considering CCS retrofit cost estimates. The notable

exception to this is data from the ScottishPower FEED Study. The literature search

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covered the full range of CO2 capture technologies – post-combustion, pre-combustion

and oxyfuel – applied to both coal- and gas-fired power plants. However, biomass CCS

and industrial applications were not considered.

Sources: Numerical data was drawn from 36 studies, namely (PB Power, 2010, IEA, 2010,

PB Power, 2011, Mott MacDonald, 2010, Mott MacDonald, 2011, Kheshgi et al., 2010,

IPCC, 2005, EPA, 2010, Fluor, 2004, Gerbelová et al., 2011, Giovanni and Richards,

2010, David and Herzog, 2000, Narula et al., 2002, Rubin et al., 2004, Rubin et al.,

2007a, Global CCS Institute, 2011b, Martinsen et al., 2007, DoE/NETL, 2010, Rubin et

al., 2007b, Hamilton et al., 2009, Davison, 2007, MIT, 2007, WorleyParsons, 2009, van

den Broek, 2009, EPRI, 2007, DoE/NETL, 2007, Marsh et al., 2003, Chen and Rubin,

2009, Julianne M, 2009, Chan et al., 2011, IEA GHG, 2008, Falcke et al., 2011, Ordorica-

Garcia et al., 2006, Viebahn et al., 2007, CCC, 2008, DTI, 2006). The following sources

were additionally used for data specifically on fossil fuel generating plant (not for CCS

data): (PB Power, 2004, PB Power, 2006, SKM, 2008). Some of the more recent estimates

are updates of previous figures by the same source – for instance the 2011 report by the

consultancy Mott MacDonald builds on the findings of its 2010 report.

Treatment of data

Normalisation: All cost estimates were converted into 2011 GBP (an average of Jan-Nov

2011 since December figures were not available at the time of calculation). The data was

not normalised to account for differences in discount rate, load factors, carbon price

etc.

Selection of representative cost estimates: Some data sources provided multiple data

estimates. In order to prevent any particular source (i.e. a source with extensive

sensitivity/scenario analysis) from dominating the data and thus skewing the results,

efforts were made to select key ‘representative’ cost estimates from each source:

Where the same source provided multiple cost estimates resulting from

sensitivity or scenario analysis, the ‘medium’ or ‘baseline/reference’ figure was

selected, or – where this did not exist – an average was calculated.

Where the source provided estimates for both (a) carbon capture and (b) full CCS,

the full CCS figure was plotted. Nb. Almost half of the sources only provided

estimates for carbon capture – with CO2 transportation and storage costs not

being factored in.

Where both first-of-a-kind (FOAK) and nth-of-a-kind (NOAK) figures were

presented the FOAK figure was selected for current cost estimates. This is

because CCS is as yet undemonstrated at utility scale.

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However, where the source provided estimates for different CCS technologies, a

representative figure for each technology was plotted. Similarly, where the source

provided estimates for multiple countries, a representative figure for each country was

plotted.

To summarise, the data reflects the full variation arising from location and CCS

technology type, but does not reflect the range of uncertainties arising from other

parameters.

Estimate year: For current cost estimates, often the year to which the estimate applied

differed from the year of publication. This typically occurred with academic papers, due

to the time lags entailed by the peer review process. Where this difference occurred, the

data analysis was based on the year to which the cost estimate applied, rather than the

publication year.

Documents

UKERC Technology and Policy Assessment Cost Methodologies